Time-Based Feedback Descriptor Analysis for Real-Time Subject Changes
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Solution Overview
Problem
Conventional feedback data processing techniques are time- and labor-intensive, require manual review, and fail to capture actual subjects or analyze changes over time, limiting the ability to recognize trends and develop proactive solutions.
Innovation Solution
A system utilizing artificial intelligence and natural language processing to automate feedback data processing, classifying, filtering, and reducing data by time periods, enabling display on graphical user interfaces for easy access and trend identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review and search techniques are used to process feedback data, then data accuracy can be maintained, but processing time and labor requirements increase significantly
Solution Approach 1:
The patent introduces natural language processing algorithms and machine learning models as intermediary systems between raw feedback data and human analysts. These intermediaries automatically classify, filter, and summarize feedback data, maintaining accuracy while dramatically reducing processing time and labor requirements.
Solution Approach 2:
The patent replaces manual mechanical review processes with automated computational systems. Natural language processing algorithms and text analysis tools substitute human analysts for initial data processing tasks, enabling rapid processing of large volumes of feedback data while preserving accuracy through multiple validation layers.
2Ease of manufacture
If predefined subjects are established prior to feedback generation, then data categorization is simplified, but the system cannot capture actual subjects that arise in the feedback data
Solution Approach 1:
The patent implements dynamic subject categorization where the system evolves from static predefined subjects to adaptive, data-driven subject identification. Machine learning models analyze feedback content to automatically discover and categorize emerging subjects, allowing the classification system to adapt dynamically to new topics and issues while maintaining organizational structure.
Solution Approach 2:
The patent segments the feedback analysis process into multiple stages: initial classification using predefined subjects, followed by detailed content analysis to identify actual subjects, and finally hierarchical organization that combines both approaches. This segmentation allows the system to benefit from both predefined categorization simplicity and actual subject capture accuracy.
3Loss of information
If all feedback data is retained and analyzed in detail, then comprehensive analysis is achieved, but the system cannot efficiently identify trends and changes over time
Solution Approach 1:
The patent extracts key features, themes, and patterns from comprehensive feedback data using natural language processing and text mining techniques. Rather than analyzing all raw data in detail, the system extracts representative indicators and metrics that capture essential information, enabling efficient trend identification while preserving analytical comprehensiveness through targeted feature selection.
Solution Approach 2:
The patent performs preliminary processing of feedback data including automatic summarization, key phrase extraction, and initial classification before trend analysis. This preliminary action organizes comprehensive data into structured formats that facilitate efficient temporal pattern recognition and trend identification without losing important information.
Data Source
AI summary
Disclosed are systems and methods that automatically classify, filter, and reduce large volumes of feedback data as a function of time using artificial intelligence technology. The aggregated feedback data is reduced by representing the feedback data as sets of descriptors corresponding to one or more time periods that are displayed on a graphical user interface. Feedback data packets are parsed by labeling the feedback data packets with a time period identifier. The feedback data packets are processed utilizing neural network technology to classify the feedback data according to one or more subject identifiers that are each associated with a subject vector. A descriptor analysis is used to process the subject vectors and the feedback data packets to generate descriptor sets comprising one or more descriptors as well as weighting data for each descriptor.